In order to improve the prediction performance of taxi-out time,various factors impacting taxi-out time were analyzed,and then two kinds of features(surface operating conditions and meteorological conditions)were intro-duced into our taxi-out prediction models,which were built based on ensemble learning algorithms including bagging method,random forest,Adaptive Boosting and Gradient Boost Machine.Taking JFK as an example,performance met-rics such as coefficient of determination,RMSE,and MAE were used to verify the prediction performance of the algo-rithms.The experimental results show that the introduction of meteorological features can improve the prediction accu-racy of taxi-out time;the prediction errors of ensemble learning are smaller than other regression algorithms;the learning curve under the ensemble methods are analyzed and we find that AdaBoost and GBM can effectively avoid overfitting.The research results can be used in the development and application of integrated surface management software.
关键词
空中交通流量管理/滑出时间/预测性能/集成学习
Key words
Air traffic flow management/Taxi-out time/Prediction performance/Ensemble learning
维普
万方数据